July 30, 2023

tKinesisInputAvro – Docs for ESB 7.x


Acts as consumer of an Amazon Kinesis stream to pull messages from this Kinesis

Using the Kinesis Client Library (KCL) provided by Amazon, tKinesisInputAvro consumes Avro-formatted
data from a given Amazon Kinesis stream (an ordered sequence of data
records), constructs an RDD out of this data and sends the RDD to its
following components.

tKinesisInputAvro properties for Apache Spark Streaming

These properties are used to configure tKinesisInputAvro running in the Spark Streaming Job framework.

The Spark Streaming
tKinesisInputAvro component belongs to the Messaging family.

The streaming version of this component is available in Talend Real Time Big Data Platform and in
Talend Data Fabric.

Basic settings

Schema and Edit

A schema is a row description. It defines the number of fields
(columns) to be processed and passed on to the next component. When you create a Spark
Job, avoid the reserved word line when naming the

Access key

Enter the access key ID that uniquely identifies an AWS
Account. For further information about how to get your Access Key and Secret Key,
see Getting Your AWS Access

Secret key

Enter the secret access key, constituting the security
credentials in combination with the access Key.

To enter the password, click the […] button next to the
password field, and then in the pop-up dialog box enter the password between double quotes
and click OK to save the settings.

Stream name

Enter the name of the Kinesis stream you want tKinesisInput to pull data from.

Endpoint URL

Enter the endpoint of the Kinesis service to be used. For example, https://kinesis.us-east-1.amazonaws.com. More valid Kinesis endpoint URLs
can be found at http://docs.aws.amazon.com/general/latest/gr/rande.html#ak_region.

Explicitly set authentication

Select this check box to use the explicit authentication mechanism to connect to Kinesis.
Note that this mechanism is supported by Spark V1.4+ only.

Since this security mechanism requires the AWS Region parameter to be explicitly set, you
need to enter the region value to be used in the Region
field that is displayed. For example, us-west-2.

It is recommended to use the explicit authentication to gain better security when the
Spark version you are using supports this mechanism. With this check box selected, the
access credentials are provided directly to Kinesis.

While if you leave this check box clear, an older authentication mechanism is used. This
way, the access credentials are used by Spark as context variables for Kinesis

Advanced settings

Checkpoint interval

Enter the time interval (in millisecond) at the end of which tKinesisInput saves the position of its read in the Kinesis stream.

Data records in a Kinesis stream are grouped into partitions (shards in terms of Kinesis)
and indexed with sequence numbers. A sequence number uniquely identifies the position of a
record. For further information about the terms used by Amazon in Kinesis, see http://docs.aws.amazon.com/kinesis/latest/dev/key-concepts.html.

Initial position stream

Select the starting position to read data from the stream in the absence of the Kinesis
checkpoint information.

  • Start with the oldest data: starts from the
    beginning of the stream within the limit of 24 hours.

  • Start after the most recent data: starts at
    the position after the latest data of the stream.

Storage level

Select how you want the received data to be cached. For further information about the
different levels, see https://spark.apache.org/docs/latest/programming-guide.html#rdd-persistence.

Use hierarchical mode

Select this check box to map the binary (including hierarchical) Avro schema to the
flat schema defined in the schema editor of the current component. If the Avro
message to be processed is flat, leave this check box clear.

Once selecting it, you need set the following parameter(s):

  • Local path to the avro
    : browse to the file which defines the
    schema of the Avro data to be processed.

  • Mapping: create the map
    between the schema columns of the current component and the data stored
    in the hierarchical Avro message to be handled. In the
    Node column, you need to
    enter the JSON path pointing to the data to be read from the
    Avro message.


Usage rule

This component is used as a start component and requires an output link.

At runtime, this component keeps listening to the stream and reads new messages once they
are buffered in this stream.

This component, along with the Spark Streaming component Palette it belongs to, appears
only when you are creating a Spark Streaming Job.

Note that in this documentation, unless otherwise explicitly stated, a scenario presents
only Standard Jobs, that is to say traditional
integration Jobs.

Spark Connection

In the Spark
tab in the Run
view, define the connection to a given Spark cluster for the whole Job. In
addition, since the Job expects its dependent jar files for execution, you must
specify the directory in the file system to which these jar files are
transferred so that Spark can access these files:

  • Yarn mode (Yarn client or Yarn cluster):

    • When using Google Dataproc, specify a bucket in the
      Google Storage staging bucket
      field in the Spark configuration

    • When using HDInsight, specify the blob to be used for Job
      deployment in the Windows Azure Storage
      area in the Spark

    • When using Altus, specify the S3 bucket or the Azure
      Data Lake Storage for Job deployment in the Spark
    • When using Qubole, add a
      tS3Configuration to your Job to write
      your actual business data in the S3 system with Qubole. Without
      tS3Configuration, this business data is
      written in the Qubole HDFS system and destroyed once you shut
      down your cluster.
    • When using on-premise
      distributions, use the configuration component corresponding
      to the file system your cluster is using. Typically, this
      system is HDFS and so use tHDFSConfiguration.

  • Standalone mode: use the
    configuration component corresponding to the file system your cluster is
    using, such as tHDFSConfiguration or

    If you are using Databricks without any configuration component present
    in your Job, your business data is written directly in DBFS (Databricks

This connection is effective on a per-Job basis.


Due to license incompatibility, one or more JARs required to use
this component are not provided. You can install the missing JARs for this particular
component by clicking the Install button
on the Component tab view. You can also
find out and add all missing JARs easily on the Modules tab in the
perspective of your studio. You can find more details about how to install external modules in
Talend Help Center (https://help.talend.com)

Related scenarios

No scenario is available for the Spark Streaming version of this component

Document get from Talend https://help.talend.com
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